{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 通过量子神经网络对鸢尾花进行分类\n",
    "\n",
    "[![下载Notebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_notebook.png)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/mindquantum/zh_cn/mindspore_classification_of_iris_by_qnn.ipynb)&emsp;\n",
    "[![下载样例代码](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_download_code.png)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/mindquantum/zh_cn/mindspore_classification_of_iris_by_qnn.py)&emsp;\n",
    "[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.png)](https://gitee.com/mindspore/docs/blob/master/docs/mindquantum/docs/source_zh_cn/classification_of_iris_by_qnn.ipynb)\n",
    "\n",
    "## 概述\n",
    "\n",
    "在之前的案例中，我们介绍了什么是变分量子线路，并通过一个简单的例子体验了如何搭建量子神经网络来解决一个小问题。在本文档中，我们将体验升级，将会介绍如何通过搭建量子神经网络来解决经典机器学习中的问题。我们选取的问题是：监督学习中的鸢尾花分类问题。\n",
    "\n",
    "问题描述：鸢尾花（iris）数据集是经典机器学习中常用的数据集，该数据集总共包含150个样本（分为3种不同的亚属：山鸢尾（setosa）、杂色鸢尾（versicolor）和维吉尼亚鸢尾（virginica），每个亚属各有50个样本），每个样本包含4个特征，分别为花萼长度（sepal length）、花萼宽度（sepal width）和花瓣长度（petal length）、花瓣宽度（petal width）。\n",
    "\n",
    "我们选取前100个样本（山鸢尾（setosa）和杂色鸢尾（versicolor）），并随机抽取80个样本作为训练集，通过搭建量子神经网络对量子分类器（Ansatz）进行训练，学习完成后，对剩余的20个样本进行分类测试，期望预测的准确率尽可能高。\n",
    "\n",
    "思路：我们需要将100个样本进行划分，分成80个训练样本和20个测试样本，根据训练样本的经典数据计算搭建Encoder所需的参数，然后，搭建Encoder，将训练样本的经典数据编码到量子态上，接着，搭建Ansatz，通过搭建的量子神经网络层和MindSpore的算子对Ansatz中的参数进行训练，进而得到最终的分类器，最后，对剩余的20个测试样本进行分类测试，得到预测的准确率。\n",
    "\n",
    "## 环境准备\n",
    "\n",
    "首先，我们需要导入鸢尾花的数据集，而在导入该数据集前，我们需要使用sklearn库中的datasets模块，因此读者需要检查是否安装了sklearn库，可执行如下代码进行安装。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```bash\n",
    "pip show scikit-learn\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "若无报错，则表明已安装。简单说明一下，sklearn是scikit-learn的简称，是一个基于Python的第三方模块。sklearn库集成了一些常用的机器学习方法，在进行机器学习任务时，并不需要实现算法，只需要简单的调用sklearn库中提供的模块就能完成大多数的机器学习任务。\n",
    "\n",
    "若未安装sklearn库，则可通过运行如下代码来安装。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```bash\n",
    "pip install scikit-learn\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "['setosa' 'versicolor' 'virginica']\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n",
      "(150,)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np                                        # 导入numpy库并简写为np\n",
    "from sklearn import datasets                              # 导入datasets模块，用于加载鸢尾花的数据集\n",
    "\n",
    "iris_dataset = datasets.load_iris()                       # 加载鸢尾花的数据集，并存在iris_dataset\n",
    "\n",
    "print(iris_dataset.data.shape)                            # 打印iris_dataset的样本的数据维度\n",
    "print(iris_dataset.feature_names)                         # 打印iris_dataset的样本的特征名称\n",
    "print(iris_dataset.target_names)                          # 打印iris_dataset的样本包含的亚属名称\n",
    "print(iris_dataset.target)                                # 打印iris_dataset的样本的标签的数组\n",
    "print(iris_dataset.target.shape)                          # 打印iris_dataset的样本的标签的数据维度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印可以看到，该数据集共有150个样本，每个样本均有4个特征，分别为花萼长度（sepal length）、花萼宽度（sepal width）和花瓣长度（petal length）、花瓣宽度（petal width）。同时样本包含3种不同的亚属：山鸢尾（setosa）、杂色鸢尾（versicolor）和维吉尼亚鸢尾（virginica），每个样本有对应的分类编号，0表示样本属于setosa，1表示样本属于versicolor，2表示样本属于virginica，因此有一个由150个数字组成的数组来表示样本的亚属类型。\n",
    "\n",
    "由于我们只选取前100个样本，因此执行如下命令。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 4)\n",
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "['setosa' 'versicolor']\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
      "(100,)\n"
     ]
    }
   ],
   "source": [
    "X = iris_dataset.data[:100, :].astype(np.float32)         # 选取iris_dataset的data的前100个数据，将其数据类型转换为float32，并储存在X中\n",
    "X_feature_names = iris_dataset.feature_names              # 将iris_dataset的特征名称储存在X_feature_names中\n",
    "y = iris_dataset.target[:100].astype(int)                 # 选取iris_dataset的target的前100个数据，将其数据类型转换为int，并储存在y中\n",
    "y_target_names = iris_dataset.target_names[:2]            # 选取iris_dataset的target_names的前2个数据，并储存在y_target_names中\n",
    "\n",
    "print(X.shape)                                            # 打印样本的数据维度\n",
    "print(X_feature_names)                                    # 打印样本的特征名称\n",
    "print(y_target_names)                                     # 打印样本包含的亚属名称\n",
    "print(y)                                                  # 打印样本的标签的数组\n",
    "print(y.shape)                                            # 打印样本的标签的数据维度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印可以看到，此时的数据集`X`中只有100个样本，每个样本依然有4个特征，仍为花萼长度（sepal length）、花萼宽度（sepal width）和花瓣长度（petal length）、花瓣宽度（petal width）。此时只有2种不同的亚属：山鸢尾（setosa）和杂色鸢尾（versicolor），并且每一个样本有对应的分类编号，0表示它属于setosa，1表示它属于versicolor，因此有一个由100个数字组成的数组来表示样本的亚属类型。\n",
    "\n",
    "## 数据图像化\n",
    "\n",
    "为了更加直观地了解这100个样本组成的数据集，我们画出所有样本不同特征之间组成的散点图，执行如下命令。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1656x1656 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt                                                           # 导入matplotlib.pyplot模块并简写为plt\n",
    "\n",
    "feature_name = {0: 'sepal length', 1: 'sepal width', 2: 'petal length', 3: 'petal width'} # 将不同的特征名称分别标记为0,1,2,3\n",
    "axes = plt.figure(figsize=(23, 23)).subplots(4, 4)                                        # 画出一个大小为23*23的图，包含4*4=16个子图\n",
    "\n",
    "colormap = {0: 'r', 1: 'g'}                                                               # 将标签为0的样本设为红色，标签为1的样本设为绿色\n",
    "cvalue = [colormap[i] for i in y]                                                         # 将100个样本对应的标签设置相应的颜色\n",
    "\n",
    "for i in range(4):\n",
    "    for j in range(4):\n",
    "        if i != j:\n",
    "            ax = axes[i][j]                                                               # 在[i][j]的子图上开始画图\n",
    "            ax.scatter(X[:, i], X[:, j], c=cvalue)                                        # 画出第[i]个特征和第[j]个特征组成的散点图\n",
    "            ax.set_xlabel(feature_name[i], fontsize=22)                                   # 设置X轴的名称为第[i]个特征名称，字体大小为22\n",
    "            ax.set_ylabel(feature_name[j], fontsize=22)                                   # 设置Y轴的名称为第[j]个特征名称，字体大小为22\n",
    "plt.show()                                                                                # 渲染图像，即呈现图像"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述呈现的图像可以看到，红色的点表示标签为“0”的样本，绿色的点表示标签为“1”的样本，另外，我们发现，这两类样本的不同特征还是比较容易区分的。\n",
    "\n",
    "## 数据预处理\n",
    "\n",
    "接下来，我们需要计算生成搭建Encoder时所要用到的参数，然后将数据集划分为训练集和测试集，执行如下命令。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 7)\n"
     ]
    }
   ],
   "source": [
    "alpha = X[:, :3] * X[:, 1:]           # 每一个样本中，利用相邻两个特征值计算出一个参数，即每一个样本会多出3个参数（因为有4个特征值），并储存在alpha中\n",
    "X = np.append(X, alpha, axis=1)       # 在axis=1的维度上，将alpha的数据值添加到X的特征值中\n",
    "\n",
    "print(X.shape)                        # 打印此时X的样本的数据维度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印可以看到，此时的数据集`X`中仍有100个样本，但此时每个样本却有7个特征，前4个特征值就是原来的特征值，后3个特征值就是通过上述预处理计算得到的特征值，其具体计算公式如下：\n",
    "\n",
    "$$\n",
    "X_{i+4}^{j} = X_{i}^{j} * X_{i+1}^{j}, i=0,1,2,j=1,2,...,100.\n",
    "$$\n",
    "\n",
    "最后，我们将此时的数据集分为训练集和测试集，执行如下命令。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80, 7)\n",
      "(20, 7)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split                                                   # 导入train_test_split函数，用于对数据集进行划分\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0, shuffle=True) # 将数据集划分为训练集和测试集\n",
    "\n",
    "print(X_train.shape)                                                                                   # 打印训练集中样本的数据类型\n",
    "print(X_test.shape)                                                                                    # 打印测试集中样本的数据类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印可以看到，此时的训练集有80个样本，测试集有20个样本，每个样本均有7个特征。\n",
    "\n",
    "说明：\n",
    "\n",
    "（1）append主要用于为原始数组添加一些值，一般格式如下：np.append(arr, values, axis=None)，arr是需要被添加值的数组，values就是添加到数组arr中的值，axis表示沿着哪个方向；\n",
    "\n",
    "（2）shuffle=True表示将数据集打乱，每次都会以不同的顺序返回， shuffle就是为了避免数据投入的顺序对网络训练造成影响。增加随机性，提高网络的泛化性能，避免因为有规律的数据出现，导致权重更新时的梯度过于极端，避免最终模型过拟合或欠拟合。\n",
    "\n",
    "（3）train_test_split是交叉验证中常用的函数，主要用于是从样本中随机地按比例选取训练数据集和测试数据集，一般格式如下：\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size, random_state, shuffle=True)，其中test_size表示测试样本的比例，random_state表示产生随机数的种子，shuffle=True表示将数据集打乱。\n",
    "\n",
    "## 搭建Encoder\n",
    "\n",
    "根据图示的量子线路图，我们可以在MindQuantum中搭建Encoder，将经典数据编码到量子态上。\n",
    "\n",
    "![encoder classification of iris by qnn](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/mindquantum/docs/source_zh_cn/images/encoder_classification_of_iris_by_qnn.png)\n",
    "\n",
    "在这里，我们采用的编码方式是IQP编码（Instantaneous Quantum Polynomial encoding），一般来说Encoder的编码方式不固定，可根据问题需要选择不同的编码方式，有时也会根据最后的性能对Encoder进行调整。\n",
    "\n",
    "Encoder中的参数$\\alpha_0,\\alpha_1,...,\\alpha_6$​​的值，就是用上述数据预处理中得到的7个特征值代入。​"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================Circuit Summary=================================\n",
      "|Total number of gates  : 17.                                                   |\n",
      "|Parameter gates        : 7.                                                    |\n",
      "|with 7 parameters are  :                                                       |\n",
      "|alpha0, alpha1, alpha2, alpha3, alpha4, alpha5, alpha6                        .|\n",
      "|Number qubit of circuit: 4                                                     |\n",
      "=================================================================================\n"
     ]
    },
    {
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      "text/plain": [
       "<mindquantum.io.display.circuit_svg_drawer.SVGCircuit at 0x7f36556ad790>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pylint: disable=W0104\n",
    "from mindquantum.core.circuit import Circuit         # 导入Circuit模块，用于搭建量子线路\n",
    "from mindquantum.core.circuit import UN              # 导入UN模块\n",
    "from mindquantum.core.gates import H, X, RZ          # 导入量子门H, X, RZ\n",
    "\n",
    "encoder = Circuit()                                  # 初始化量子线路\n",
    "\n",
    "encoder += UN(H, 4)                                  # H门作用在每1位量子比特\n",
    "for i in range(4):                                   # i = 0, 1, 2, 3\n",
    "    encoder += RZ(f'alpha{i}').on(i)                 # RZ(alpha_i)门作用在第i位量子比特\n",
    "for j in range(3):                                   # j = 0, 1, 2\n",
    "    encoder += X.on(j+1, j)                          # X门作用在第j+1位量子比特，受第j位量子比特控制\n",
    "    encoder += RZ(f'alpha{j+4}').on(j+1)             # RZ(alpha_{j+4})门作用在第0位量子比特\n",
    "    encoder += X.on(j+1, j)                          # X门作用在第j+1位量子比特，受第j位量子比特控制\n",
    "\n",
    "encoder = encoder.no_grad()                          # Encoder作为整个量子神经网络的第一层，不用对编码线路中的梯度求导数，因此加入no_grad()\n",
    "encoder.summary()                                    # 总结Encoder\n",
    "encoder.svg()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从对Encoder的Summary中可以看到，该量子线路由17个量子门组成，其中有7个含参量子门且参数为$\\alpha_0,\\alpha_1,...,\\alpha_6$，该量子线路调控的量子比特数为4。\n",
    "\n",
    "说明：\n",
    "\n",
    "UN模块用于将量子门映射到不同的目标量子比特和控制量子比特，一般格式如下：mindquantum.circuit.UN(gate, maps_obj, maps_ctrl=None)，括号中的gate是我们需要执行的量子门，maps_obj是需要执行该量子门的目标量子比特，maps_ctrl是控制量子比特，若为None即无控制量子位。若每个量子比特位执行同一个非参数量子门，则可以直接写出UN(gate, N)，N表示量子比特个数。\n",
    "\n",
    "## 搭建Ansatz\n",
    "\n",
    "根据图示的量子线路图，我们可以在MindQuantum中搭建Ansatz。\n",
    "\n",
    "![ansatz classification of iris by qnn](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/docs/mindquantum/docs/source_zh_cn/images/ansatz_classification_of_iris_by_qnn.png)\n",
    "\n",
    "与Encoder一样，Ansatz的编码方式也不固定，我们可以尝试不同的编码方式来测试最后的结果。\n",
    "\n",
    "在这里，我们采用的是HardwareEfficientAnsatz，即上述量子线路图所示的编码方式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================================================Circuit Summary===================================================\n",
      "|Total number of gates  : 25.                                                                                       |\n",
      "|Parameter gates        : 16.                                                                                       |\n",
      "|with 16 parameters are :                                                                                           |\n",
      "|d0_n0_0, d0_n1_0, d0_n2_0, d0_n3_0, d1_n0_0, d1_n1_0, d1_n2_0, d1_n3_0, d2_n0_0, d2_n1_0..                        .|\n",
      "|Number qubit of circuit: 4                                                                                         |\n",
      "=====================================================================================================================\n"
     ]
    },
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      "text/plain": [
       "<mindquantum.io.display.circuit_svg_drawer.SVGCircuit at 0x7f35c1b23910>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pylint: disable=W0104\n",
    "from mindquantum.algorithm.nisq import HardwareEfficientAnsatz                                      # 导入HardwareEfficientAnsatz\n",
    "from mindquantum.core.gates import RY                                                               # 导入量子门RY\n",
    "\n",
    "ansatz = HardwareEfficientAnsatz(4, single_rot_gate_seq=[RY], entangle_gate=X, depth=3).circuit     # 通过HardwareEfficientAnsatz搭建Ansatz\n",
    "ansatz.summary()                                                                                    # 总结Ansatz\n",
    "ansatz.svg()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从对Ansatz的Summary中可以看到，该量子线路由25个量子门组成，其中有16个含参量子门且参数为d2_n3_0, d1_n1_0, d0_n2_0, d1_n0_0, d3_n2_0, d2_n2_0, d0_n1_0, d3_n1_0, d2_n0_0, d3_n0_0...，该量子线路调控的量子比特数为4。\n",
    "\n",
    "说明：\n",
    "\n",
    "HardwareEfficientAnsatz是一种容易在量子芯片上实现的Ansatz，其量子线路图由红色虚线框内的量子门组成，一般格式如下：mindquantum.ansatz.HardwareEfficientAnsatz(n_qubits, single_rot_gate_seq, entangle_gate=X, entangle_mapping=\"linear\", depth=1)，括号中的n_qubits表示ansatz需要作用的量子比特总数，single_rot_gate_seq表示一开始每一位量子比特执行的参数门，同时后面需要执行的参数门也固定了，只是参数不同，entangle_gate=X表示执行的纠缠门为X，entangle_mapping=\"linear\"表示纠缠门将作用于每对相邻量子比特，depth表示黑色虚线框内的量子门需要重复的次数。\n",
    "\n",
    "那么完整的量子线路就是Encoder加上Ansatz。这里我们调用量子线路的`as_encoder`将量子线路中的所有参数设置为编码参数，调用`as_ansatz`将量子线路中的所有参数设置为待训练参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================Circuit Summary================================================\n",
      "|Total number of gates  : 42.                                                                                 |\n",
      "|Parameter gates        : 23.                                                                                 |\n",
      "|with 23 parameters are :                                                                                     |\n",
      "|alpha0, alpha1, alpha2, alpha3, alpha4, alpha5, alpha6, d0_n0_0, d0_n1_0, d0_n2_0..                        . |\n",
      "|Number qubit of circuit: 4                                                                                   |\n",
      "===============================================================================================================\n"
     ]
    },
    {
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      "text/plain": [
       "<mindquantum.io.display.circuit_svg_drawer.SVGCircuit at 0x7f35ebee16d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pylint: disable=W0104\n",
    "circuit = encoder.as_encoder() + ansatz.as_ansatz()                  # 完整的量子线路由Encoder和Ansatz组成\n",
    "circuit.summary()\n",
    "circuit.svg()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从对完整的量子线路的Summary中可以看到，该量子线路由42个量子门组成，其中有23个含参量子门且参数为$\\alpha_0,\\alpha_1,...,\\alpha_6$和d2_n3_0, d1_n1_0, d0_n2_0, d1_n0_0, d3_n2_0, d2_n2_0, d0_n1_0, d3_n1_0, d2_n0_0, d3_n0_0...，该量子线路调控的量子比特数为4。\n",
    "\n",
    "## 构建哈密顿量\n",
    "\n",
    "我们分别对第2位和第3位量子比特执行泡利`Z`算符测量，构建对应的哈密顿量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 [Z2] \n",
      "1 [Z3] \n"
     ]
    }
   ],
   "source": [
    "from mindquantum.core.operators import QubitOperator           # 导入QubitOperator模块，用于构造泡利算符\n",
    "from mindquantum.core.operators import Hamiltonian             # 导入Hamiltonian模块，用于构建哈密顿量\n",
    "\n",
    "hams = [Hamiltonian(QubitOperator(f'Z{i}')) for i in [2, 3]]   # 分别对第2位和第3位量子比特执行泡利Z算符测量，且将系数都设为1，构建对应的哈密顿量\n",
    "for h in hams:\n",
    "    print(h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印可以看到，此时构建的哈密顿量有2个，分别为对第2位和第3位量子比特执行泡利`Z`算符，且将系数都设为1。通过泡利`Z`算符测量，我们可以得到2个哈密顿量测量值，若第1个测量值更大，则会将此样本归类到标签为“0”的类，同理，若第2个测量值更大，则会将此样本归类到标签为“1”的类。通过神经网络的训练，期望训练样本中标签为“0”的样本的第1个测量值更大，而标签为“1”的样本的第2个测量值更大，最后应用此模型来预测新样本的分类。\n",
    "\n",
    "## 搭建量子神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MQLayer<\n",
       "  (evolution): MQOps<4 qubits mqvector VQA Operator>\n",
       "  >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pylint: disable=W0104\n",
    "import mindspore as ms                                                                         # 导入mindspore库并简写为ms\n",
    "from mindquantum.framework import MQLayer                                                      # 导入MQLayer\n",
    "from mindquantum.simulator import Simulator\n",
    "\n",
    "ms.set_context(mode=ms.PYNATIVE_MODE, device_target=\"CPU\")\n",
    "ms.set_seed(1)                                                                                 # 设置生成随机数的种子\n",
    "sim = Simulator('mqvector', circuit.n_qubits)\n",
    "grad_ops = sim.get_expectation_with_grad(hams,\n",
    "                                         circuit,\n",
    "                                         parallel_worker=5)\n",
    "QuantumNet = MQLayer(grad_ops)          # 搭建量子神经网络\n",
    "QuantumNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印可以看到，我们已经成功搭建了量子机器学习层，其可以无缝地跟MindSpore中其它的算子构成一张更大的机器学习网络。\n",
    "\n",
    "说明：\n",
    "\n",
    "MindSpore是一个全场景深度学习框架，旨在实现易开发、高效执行、全场景覆盖三大目标，提供支持异构加速的张量可微编程能力，支持云、服务器、边和端多种硬件平台.\n",
    "\n",
    "## 训练\n",
    "\n",
    "接下来，我们需要定义损失函数，设定需要优化的参数，然后将搭建好的量子机器学习层和MindSpore的算子组合，构成一张更大的机器学习网络，最后对该模型进行训练。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[WARNING] ME(130244:139871534524224,MainProcess):2022-10-28-17:22:15.281.374 [mindspore/train/model.py:1075] For StepAcc callback, {'step_end'} methods may not be supported in later version, Use methods prefixed with 'on_train' or 'on_eval' instead when using customized callbacks.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 step: 16, loss is 0.6140301823616028\n",
      "epoch: 2 step: 16, loss is 0.48262983560562134\n",
      "epoch: 3 step: 16, loss is 0.43457236886024475\n",
      "epoch: 4 step: 16, loss is 0.4101267457008362\n",
      "epoch: 5 step: 16, loss is 0.4027639925479889\n",
      "epoch: 6 step: 16, loss is 0.39859312772750854\n",
      "epoch: 7 step: 16, loss is 0.39496558904647827\n",
      "epoch: 8 step: 16, loss is 0.3970319926738739\n",
      "epoch: 9 step: 16, loss is 0.3954522907733917\n",
      "epoch: 10 step: 16, loss is 0.39520972967147827\n",
      "epoch: 11 step: 16, loss is 0.3955090641975403\n",
      "epoch: 12 step: 16, loss is 0.3953099250793457\n",
      "epoch: 13 step: 16, loss is 0.39525243639945984\n",
      "epoch: 14 step: 16, loss is 0.3952508568763733\n",
      "epoch: 15 step: 16, loss is 0.39521533250808716\n",
      "epoch: 16 step: 16, loss is 0.39519912004470825\n",
      "epoch: 17 step: 16, loss is 0.39518338441848755\n",
      "epoch: 18 step: 16, loss is 0.395169198513031\n",
      "epoch: 19 step: 16, loss is 0.39515653252601624\n",
      "epoch: 20 step: 16, loss is 0.3951443135738373\n"
     ]
    }
   ],
   "source": [
    "from mindspore.nn import SoftmaxCrossEntropyWithLogits                         # 导入SoftmaxCrossEntropyWithLogits模块，用于定义损失函数\n",
    "from mindspore.nn import Adam, Accuracy                                        # 导入Adam模块和Accuracy模块，分别用于定义优化参数，评估预测准确率\n",
    "import mindspore as ms\n",
    "from mindspore.dataset import NumpySlicesDataset                               # 导入NumpySlicesDataset模块，用于创建模型可以识别的数据集\n",
    "\n",
    "loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')            # 通过SoftmaxCrossEntropyWithLogits定义损失函数，sparse=True表示指定标签使用稀疏格式，reduction='mean'表示损失函数的降维方法为求平均值\n",
    "opti = Adam(QuantumNet.trainable_params(), learning_rate=0.1)                  # 通过Adam优化器优化Ansatz中的参数，需要优化的是Quantumnet中可训练的参数，学习率设为0.1\n",
    "\n",
    "model = ms.Model(QuantumNet, loss, opti, metrics={'Acc': Accuracy()})             # 建立模型：将MindQuantum构建的量子机器学习层和MindSpore的算子组合，构成一张更大的机器学习网络\n",
    "\n",
    "train_loader = NumpySlicesDataset({'features': X_train, 'labels': y_train}, shuffle=False).batch(5) # 通过NumpySlicesDataset创建训练样本的数据集，shuffle=False表示不打乱数据，batch(5)表示训练集每批次样本点有5个\n",
    "test_loader = NumpySlicesDataset({'features': X_test, 'labels': y_test}).batch(5)                   # 通过NumpySlicesDataset创建测试样本的数据集，batch(5)表示测试集每批次样本点有5个\n",
    "\n",
    "\n",
    "class StepAcc(ms.Callback):                                                      # 定义一个关于每一步准确率的回调函数\n",
    "    def __init__(self, model, test_loader):\n",
    "        self.model = model\n",
    "        self.test_loader = test_loader\n",
    "        self.acc = []\n",
    "\n",
    "    def step_end(self, run_context):\n",
    "        self.acc.append(self.model.eval(self.test_loader, dataset_sink_mode=False)['Acc'])\n",
    "\n",
    "\n",
    "monitor = ms.LossMonitor(16)                                                    # 监控训练中的损失，每16步打印一次损失值\n",
    "\n",
    "acc = StepAcc(model, test_loader)                                               # 使用建立的模型和测试样本计算预测的准确率\n",
    "\n",
    "model.train(20, train_loader, callbacks=[monitor, acc], dataset_sink_mode=False)# 将上述建立好的模型训练20次"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印可以看到，20次迭代后，损失值不断下降并趋于稳定，最后收敛于约0.395。\n",
    "\n",
    "说明：\n",
    "\n",
    "（1）nn.SoftmaxCrossEntropyWithLogits可以计算数据和标签之间的softmax交叉熵。使用交叉熵损失测量输入（使用softmax函数计算）的概率和目标之间的分布误差，其中类是互斥的（只有一个类是正的），一般格式如下：mindspore.nn.SoftmaxCrossEntropyWithLogits(sparse=False, reduction=\"none\")，sparse=False表示指定标签是否使用稀疏格式，默认值:False；reduction=\"none\"表示适用于损失的减少类型。可选值为mean、sum和none。如果为none，则不执行减少，默认值:“没有”。\n",
    "\n",
    "（2）Adam模块通过自适应矩估计算法更新梯度，可以优化Ansazt中的参数，输入的是神经网络中可训练的参数；一般格式如下：nn.Adam(net.trainable_params(), learning_rate=0.1)，学习率可以自己调节；\n",
    "\n",
    "（3）mindspore.train.Model是用于训练或测试的高级API，模型将层分组到具有训练和推理特征的对象中，一般格式如下：mindspore.train.Model(network, loss_fn=None, optimizer=None, metrics=None, eval_network=None, eval_indexes=None, amp_level=\"O0\", acc_level=\"O0\")，其中network就是我们要训练的网络即Quantumnet；loss_fn即目标函数，在这里就是定义的loss函数；optimizer即优化器，用于更新权重，在这里就是定义的opti；metrics就是模型在训练和测试期间需要评估的字典或一组度量，在这里就是评估准确率；\n",
    "\n",
    "（4）Accuracy用于计算分类和多标签数据的准确率，一般格式如下：mindspore.nn.Accuracy(eval_type=\"classification\")，用于分类（单标签）和多标签（多标签分类)）的数据集上计算准确率的度量，默认值：“分类”；\n",
    "\n",
    "（5）NumpySlicesDataset使用给定的数据切片创建数据集，主要用于将Python数据加载到数据集中，一般格式如下：mindspore.dataset.NumpySlicesDataset(data, column_names=None, num_samples=None, num_parallel_workers=1, shuffle=None, sampler=None, num_shards=None, shard_id=None)；\n",
    "\n",
    "（6）Callback用于构建回调类的抽象基类，回调是上下文管理器，在传递到模型时将输入和输出。你可以使用此机制自动初始化和释放资源。回调函数将执行当前步骤或数据轮回中的一些操作；\n",
    "\n",
    "（7）LossMonitor主要用于监控训练中的损失，如果损失是NAN或INF，它将终止训练，一般格式如下：mindspore.LossMonitor(per_print_times=1)，per_print_times=1表示每秒钟打印一次损失，默认值：1；\n",
    "\n",
    "（8）train模块用于训练模型，其中迭代由Python前端控制；当设置PyNative模式或CPU时，训练过程将在数据集不接收的情况下执行，一般格式如下：train(epoch, train_dataset, callbacks=None, dataset_sink_mode=True, sink_size=-1)，其中epoch表示在数据上的总迭代次数；train_dataset就是我们定义的train_loader；callbacks就是我们需要回调的损失值和准确率；dataset_sink_mode表示确定是否通过数据集通道传递数据，文档中为否。\n",
    "\n",
    "## 训练过程中的准确率\n",
    "\n",
    "我们已经看到损失值趋于稳定，那么我们还可以将模型在训练过程中的预测的准确率呈现出来，执行如下代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(acc.acc)\n",
    "plt.title('Statistics of accuracy', fontsize=20)\n",
    "plt.xlabel('Steps', fontsize=20)\n",
    "plt.ylabel('Accuracy', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印的图像可以看到，在大约50步后，预测的准确率收敛于1，也就是说预测的准确率已经可以达到100%。\n",
    "\n",
    "## 预测\n",
    "\n",
    "最后，我们测试一下训练好的模型，将其应用在测试集上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测分类结果： [0 1 0 1 1 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0]\n",
      "实际分类结果： [0 1 0 1 1 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0]\n",
      "{'Acc': 1.0}\n"
     ]
    }
   ],
   "source": [
    "from mindspore import ops                                         # 导入ops模块\n",
    "\n",
    "predict = np.argmax(ops.Softmax()(model.predict(ms.Tensor(X_test))), axis=1)    # 使用建立的模型和测试样本，得到测试样本预测的分类\n",
    "correct = model.eval(test_loader, dataset_sink_mode=False)                   # 计算测试样本应用训练好的模型的预测准确率\n",
    "\n",
    "print(\"预测分类结果：\", predict)                                              # 对于测试样本，打印预测分类结果\n",
    "print(\"实际分类结果：\", y_test)                                               # 对于测试样本，打印实际分类结果\n",
    "\n",
    "print(correct)                                                               # 打印模型预测的准确率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述打印的可以看到，预测分类结果和实际分类结果完全一致，模型预测的准确率达到了100%。\n",
    "\n",
    "至此，我们体验了如何通过搭建量子神经网络来解决经典机器学习中的经典问题——鸢尾花分类问题。相信大家也对使用MindQuantum有了更进一步的了解！期待大家挖掘更多的问题，充分发挥MindQuantum强大的功能！\n",
    "\n",
    "若想查询更多关于MindQuantum的API，请点击：[https://mindspore.cn/mindquantum/](https://mindspore.cn/mindquantum/)。"
   ]
  }
 ],
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   "language": "python",
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